Privacy-preserving Model Training for Disease Prediction Using Federated Learning with Differential Privacy-Reference-Cited by-同舟云学术

Privacy-preserving Model Training for Disease Prediction Using Federated Learning with Differential Privacy

Published:2022-07-11 Issue: Volume: Page:
ISSN:
Container-title:2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Khanna Amol¹,Schaffer Vincent²,Gursoy Gamze³,Gerstein Mark⁴

Affiliation:

1. Johns Hopkins University,Department of Biomedical Engineering, Department of Applied Mathematics and Statistics,Baltimore,MD,USA,21218

2. Yale University,Department of Computer Science,New Haven,CT,USA,06520

3. Columbia University,Department of Biomedical Informatics,New York,NY,USA,10032

4. Program in Computational Biology and Bioinformatics, Yale University,Department of Molecular Biophysics and Biochemistry, Department of Computer Science, Department of Statistics and Data Science,New Haven,CT,USA,06250

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871742.pdf?arnumber=9871742

Reference22 articles.

1. Federated Learning in a Medical Context: A Systematic Literature Review

2. Robust de-anonymization of large datasets (how to break anonymity of the Netflix prize dataset);narayanan;ArXiv,2008

3. The secret sharer: evaluation and testing unintended memorization in neural networks;carlini;ArXiv,2019

4. The Algorithmic Foundations of Differential Privacy

5. A general approach to adding differential privacy to iterative training procedures;mcmahan;ArXiv,2019

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